Abstract

To achieve superior image reconstruction, this paper investigates a hybrid regularizers model for image denoising and deblurring. This approach closely incorporates the advantages of the total generalized variation and wavelet frame-based methods. Computationally, a highly efficient alternating minimization algorithm containing no inner iterations is introduced in detail, which synchronously restores the degraded image and automatically estimates the regularization parameter based on Morozov’s discrepancy principle. Illustrationally, we demonstrate that our proposed strategy significantly outperforms several current state-of-the-art numerical methods and closely matches the performance of human vision in solving the image deconvolution problem, with respect to restoration accuracy, staircase artifacts suppression and features preservation.